Halotolerant Ability and α-Amylase Activity of Some Saltwater Fungal Isolates

Four halotolerant fungal isolates originating from the saltwater Lake Urmia in Iran were selected during a screening program for salt resistance and α-amylase activity. The isolates were identified based on sequencing the ITS region and a part of the β-tubulin gene, as Penicillium chrysogenum (isolate U1; CBS 132820), Fusarium incarnatum (isolate U2; CBS 132821), and Penicillium polonicum (isolate U3; CBS 132822, and isolate U4; CBS 132823). The growth of these isolates was determined by measuring the colony diameter and mycelia dry weight in Sabouraud dextrose agar and yeast nitrogen base medium supplemented with NaCl, KCl, and LiCl. Isolate U4 showed a growth up in 15% NaCl and U1 was the only isolate that could grow in 20% KCl. None of the strains grew in a media containing LiCl. The salt supplemented medium did not increase the size of colony diameter in all isolates (p > 0.05). The ability of the selected isolates for amylase production was quantitatively tested and showed that P. polonicum isolate U4 was the most potent producer of amylase with a yield of 260.9 U/L after 60 h, whereas P. polonicum isolate U3 was the lowest one with a production level of 97.9 U/L after 48 h. P. polonicum isolate U4 could be a suitable candidate for production of amylase on an industrial scale after optimization. © 2013 by School of Pharmacy

Group method of data handling to predict scour depth around vertical piles under regular waves

AbstractThis paper presents a new application of the Group Method Of Data Handling (GMDH), to predict pile scour depth exposed to waves. The GMDH network was developed using the Levenberg–Marquardt (LM) method in the training stage for scour prediction. Scour depth due to regular waves was modeled as a function of five dimensionless parameters, including pile Reynolds number, grain Reynolds number, sediment number, Keulegan–Carpenter number, and shields parameter. The testing results of the GMDH-LM were compared with those obtained using the Adaptive Neuro-Fuzzy Inference System (ANFIS), Radial Basis Function-Neural Network (RBF-NN), and empirical equations. In particular, the GMDH-LM provided the most accurate prediction of scour depth compared to other models. Also, the Keulegan–Carpenter number has been determined as the most effective parameter on scour depth through a sensitivity analysis. The GMDH-LM was utilized successfully to investigate the influence of the pile cross section and Keulegan–Carpenter number on scour depth

Cost-Effectiveness of Using a Molecular Diagnostic Test to Improve Preoperative Diagnosis of Thyroid Cancer

AbstractObjectiveFine-needle aspiration biopsy (FNAB) is a safe and inexpensive diagnostic procedure for evaluating thyroid nodules.Up to 25% of the results from an FNAB, however, may not be diagnostic or may be indeterminate, leading to a subsequent diagnostic thyroid surgery. A new molecularly based diagnostic test could potentially reduce indeterminate cytological results and, with high accuracy, provide a definitive diagnosis for cancer in thyroid nodules. The aim of the study was to estimate the cost-effectiveness of utilizing a molecular diagnostic (DX) test as an adjunct to FNAB, compared with NoDX, to improve the preoperative diagnosis of thyroid nodules.MethodsWe constructed a patient-level simulation model to estimate the clinical and economic outcomes of using a DX test compared with current practice (NoDX) for the diagnosis of thyroid nodules. By using a cost-effectiveness framework, we measured incremental clinical benefits in terms of quality-adjusted life-years and incremental costs over a 10-year time horizon.ResultsAssuming 95% sensitivity and specificity of the Dx test when used as an adjunct to FNAB, the utilization of the DX test resulted in a gain of 0.046 quality-adjusted life-years (95% confidence interval 0.019–0.078) and a saving of $1087 (95$691–$1533) in direct costs per patient. If the cost of the Dx test is less than$1087 per test, we expect to save quality-adjusted life-years and reduce costs when it is utilized. Sensitivity of the DX test, compared with specificity, had a larger influence on the overall outcomes

Deep Boosting Multi-Modal Ensemble Face Recognition with Sample-Level Weighting

Deep convolutional neural networks have achieved remarkable success in face recognition (FR), partly due to the abundant data availability. However, the current training benchmarks exhibit an imbalanced quality distribution; most images are of high quality. This poses issues for generalization on hard samples since they are underrepresented during training. In this work, we employ the multi-model boosting technique to deal with this issue. Inspired by the well-known AdaBoost, we propose a sample-level weighting approach to incorporate the importance of different samples into the FR loss. Individual models of the proposed framework are experts at distinct levels of sample hardness. Therefore, the combination of models leads to a robust feature extractor without losing the discriminability on the easy samples. Also, for incorporating the sample hardness into the training criterion, we analytically show the effect of sample mining on the important aspects of current angular margin loss functions, i.e., margin and scale. The proposed method shows superior performance in comparison with the state-of-the-art algorithms in extensive experiments on the CFP-FP, LFW, CPLFW, CALFW, AgeDB, TinyFace, IJB-B, and IJB-C evaluation datasets.Comment: 2023 IEEE International Joint Conference on Biometrics (IJCB

AAFACE: Attribute-aware Attentional Network for Face Recognition

In this paper, we present a new multi-branch neural network that simultaneously performs soft biometric (SB) prediction as an auxiliary modality and face recognition (FR) as the main task. Our proposed network named AAFace utilizes SB attributes to enhance the discriminative ability of FR representation. To achieve this goal, we propose an attribute-aware attentional integration (AAI) module to perform weighted integration of FR with SB feature maps. Our proposed AAI module is not only fully context-aware but also capable of learning complex relationships between input features by means of the sequential multi-scale channel and spatial sub-modules. Experimental results verify the superiority of our proposed network compared with the state-of-the-art (SoTA) SB prediction and FR methods.Comment: Accepted to $30^{th}$ IEEE International Conference on Image Processing (ICIP 2023) as an oral presentatio

The Effect of Mesalazine on Pain Management in Patients with Diarrhea-Predominant Irritable Bowel Syndrome

Background and Objective: Irritable bowel syndrome is one of the common gastrointestinal complaints that is often resistant to standard treatment. Since mesalazine reduces abdominal pain and diarrhea through its anti-inflammatory effects, this study was conducted to investigate the effect of mesalazine on pain management in patients with diarrhea-predominant irritable bowel syndrome (IBS-D). Methods: This case-control study was conducted among 100 patients diagnosed with diarrhea-predominant irritable bowel syndrome referred to Ayatollah Rouhani Hospital in Babol. Patients were randomly divided into two groups of 50, receiving standard treatment (amitriptyline 25 mg) and a group that received mesalazine at a dose of 500 mg for 4 weeks three times a day in addition to standard treatment. Subjects were examined and compared in terms of pain intensity and frequency of discharge and recovery. Findings: The intensity of pain after treatment in the case group was lower than before treatment (4.66±2.29 vs. 7.80±2.01) (p=0.004). Out of 50 patients, 38 people (76.0%) in the case group and only 20 people (40%) in the control group recovered (p<0.001). In the case group, the number of bowel movements after treatment (2.20±0.92) was significantly less than before (4.92±0.92) (p<0.001). In the control group, the number of bowel movements showed a statistically significant difference before and after treatment (5.22±1.07 vs. 2.50±1.19) (p<0.001). Conclusion: Based on the results of this study, mesalazine is significantly effective in reducing the intensity of pain caused by irritable bowel syndrome. However, there was no difference in the number of excretions

Effects of silibinin on hair follicle stem cells differentiation to neural-like cells

The hair follicle stem cells are an abundant and easily accessible source of pluripotent adult stem cells. Hairfollicle stem cells differentiate into neurons and glial cells in vitro and express stem cell markers such as tubulin, RIP, P75, and S100. Silibinin, a flavonolignan and the active component of Milk thistle (Silybum Marianum) has been used as a dietary supplement and herbal medication. The aim of the present study was to investigate the effects of Silibinin on hair follicle stem cells differentiation to neural-like cells. The bulge region of the rat whisker was isolated and cultured in Dulbecco's modified Eagle's medium supplemented with different concentrations of silibinin and Neurotrophin-3. One week later, bulge cells immunostained with Nestin and p75 and then with tubulin. The morphological and biological features of cultured bulge cells were observed by light microscopy. Present data demonstrated that silibinin (0.5 μg mL-1) can promote differentiation of hair follicle stem cells to neural-like cells. © 2011 Academic Journals Inc

Ogre and Pythia: An Invariance Proof Method for Weak Consistency Models

We design an invariance proof method for concurrent programs parameterised by a weak consistency model. The calculational design of the invariance proof method is by abstract interpretation of a truly parallel analytic semantics. This generalises the methods by Lamport and Owicki-Gries for sequential consistency. We use cat as an example of language to write consistency specifications of both concurrent programs and machine architectures